Despite the wide application of longitudinal studies, they are often plagued by missing data and attrition. The majority of methodological approaches focus on participant retention or modern missing data analysis procedures. This paper, however, takes a new approach by examining how researchers may supplement the sample with additional participants. First, refreshment samples use the same selection criteria as the initial study. Second, replacement samples identify auxiliary variables that may help explain patterns of missingness and select new participants based on those characteristics. A simulation study compares these two strategies for a linear growth model with five measurement occasions. Overall, the results suggest that refreshment samples lead to less relative bias, greater relative efficiency, and more acceptable coverage rates than replacement samples or not supplementing the missing participants in any way. Refreshment samples also have high statistical power. The comparative strengths of the refreshment approach are further illustrated through a real data example. These findings have implications for assessing change over time when researching at-risk samples with high levels of permanent attrition.

This layer provides geochemical analysis associated with offshore sampling activities. It contains analysis of 38 elements and should be used as a baseline for chemical element concentrations in seabed sediments, against which samples collected in the future may be assessed. Related data in Offshore Sample Data - Activity & Scan collection.

Abstract

Most countries collect official statistics on energy use due to its vital role in the infrastructure, economy and living standards.

In Palestine, additional attention is warranted for energy statistics due to a scarcity of natural resources, the high cost of energy and high population density. These factors demand comprehensive and high quality statistics.

In this contest PCBS decided to conduct a special Energy Consumption in Transport Survey to provide high quality data about energy consumption by type, expenditure on maintenance and insurance for vehicles, and questions on vehicles motor capacity and year of production.

The survey aimed to provide data on energy consumption by transport sector and also on the energy consumption by the type of vehicles and its motor capacity and year of production.

Geographic coverage

Palestine

Analysis unit

Vehicles

Universe

All the operating vehicles in Palestine in 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Target Population: All the operating vehicles in Palestine in 2014.

2.1Sample Frame A list of the number of the operating vehicles in Palestine in 2014, they are broken down by governorates and vehicle types, this list was obtained from Ministry of transport.

2.2.1 Sample size The sample size is 6,974 vehicles.

2.2.2 Sampling Design it is stratified random sample, and in some of the small size strata the quota sample was used to cover them.

The method of reaching the vehicles sample was through : 1-reaching to all the dynamometers (the centers for testing the vehicles) 2-selecting a random sample of vehicles by type of vehicle, model, fuel type and engine capacity

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire was based on the experiences of other similar countries in energy statistics subject to cover the most important indicators for energy statistics in transport sector, taking into account Palestine's particular situation.

Cleaning operations

The data processing stage consisted of the following operations: Editing and coding prior to data entry: all questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: The survey questionnaire was uploaded on office computers. At this stage, data were entered into the computer using a data entry template developed in Access Database. The data entry program was prepared to satisfy a number of requirements: ·To prevent the duplication of questionnaires during data entry. ·To apply checks on the integrity and consistency of entered data. ·To handle errors in a user friendly manner. ·The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS. Audit after data entered at this stage is data entered scrutiny by pulling the data entered file periodically and review the data and examination of abnormal values and check consistency between the different questions in the questionnaire, and if there are any errors in the data entered to be the withdrawal of the questionnaire and make sure this data and adjusted, even been getting the final data file that is the final extract data from it. Extraction Results: The extract final results of the report by using the SPSS program, and then display the results through tables to Excel format.

Response rate

80.7%

Sampling error estimates

Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences are anticipated in comparison with the real values obtained through censuses. The variance was calculated for the most important indicators: the variance table is attached with the final report. There is no problem in the dissemination of results at national and regional level (North, Middle, South of West Bank, Gaza Strip).

Data appraisal

The survey sample consisted of around 6,974 vehicles, of which 5,631 vehicles completed the questionnaire, 3,652 vehicles from the West Bank and 1,979 vehicles in Gaza Strip.

Abstract

The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

The full-population dataset (with about 10 million individuals) is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.

Analysis unit

Household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

ssd

Sampling procedure

The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

Response rate

This is a synthetic dataset; the "response rate" is 100%.

Sample data for exercises in Further Adventures in Data Cleaning.

This upload contains two Geopackage files of raw data used for urban analysis in the outskirts of Lille and Nice, France. 
The data include building footprints (layer "building"), roads (layer "road"), and administrative boundaries (layer "adm_boundaries")
extracted from version 3.3 of the French dataset BD TOPO®3 (IGN, 2023) for the municipalities of Santes, Hallennes-lez-Haubourdin,
Haubourdin, and Emmerin in northern France (Geopackage "DPC_59.gpkg") and Drap, Cantaron and La Trinité in southern France 
(Geopackage "DPC_06.gpkg").

Metadata for these layers is available here: https://geoservices.ign.fr/sites/default/files/2023-01/DC_BDTOPO_3-3.pdf

Additionally, this upload contains the results of the following algorithms available in GitHub (https://github.com/perezjoan/emc2-WP2?tab=readme-ov-file)

1. The identification of main streets using the QGIS plugin Morpheo (layers "road_morpheo" and "buffer_morpheo") 
https://plugins.qgis.org/plugins/morpheo/ 

2. The identification of main streets in local contexts – connectivity locally weighted (layer "road_LocRelCon")

3. Basic morphometry of buildings (layer "building_morpho")

4. Evaluation of the number of dwellings within inhabited buildings (layer "building_dwellings")

5. Projecting population potential accessible from main streets (layer "road_pop_results")

Project website: http://emc2-dut.org/

Publications using this sample data: 
Perez, J. and Fusco, G., 2024. Potential of the 15-Minute Peripheral City: Identifying Main Streets and Population Within Walking Distance. In: O. Gervasi, B. Murgante, C. Garau, D. Taniar, A.M.A.C. Rocha and M.N. Faginas Lago, eds. Computational Science and Its Applications – ICCSA 2024 Workshops. ICCSA 2024. Lecture Notes in Computer Science, vol 14817. Cham: Springer, pp.50-60. https://doi.org/10.1007/978-3-031-65238-7_4.

Acknowledgement. This work is part of the emc2 project, which received the grant ANR-23-DUTP-0003-01 from the French National Research Agency (ANR) within the DUT Partnership.

This data includes a document that describes the effort to collect and analyze water and gas samples from deep Utah FORGE wells 16A(78)-32, 58-32, 56-32 and 78B-32 along with additional pdf files showing ThermoChem's analyses attached as an appendix.

Clarivate Analytics, managers of Web of Science, publishes an annual listing of highly cited researchers. The opening sentence of the 2019 report asks, "Who would contest that in the race for knowledge it is human capital that is most essential?". They state that "talent - including intelligence, creativity, ambition, and social competence (where needed) - outpaces other capacities such as access to funding and facilities". This contradicts the findings of Sinay et al. (2019), who found that the algorithm used by search engines, including the Web of Science, is possibly more influential than human capital. Using Clarivate Analytics' database for 2018, we investigated which factors are most relevant in the impact race. Rather than human capital alone, we found that language, gender, funding and facilities introduce bias to assessments and possibly prevent talent and discoveries from emerging. We found that the profile of the highly cited scholars is so narrow that it may compromise the validity of scientific knowledge, because it is biased towards the perception and interests of male scholars affiliated with very-highly-developed countries where English is commonly spoken. These scholars accounted for 80 percent of the random sample analyzed; absent were women from Latin-America, Africa, Asia and Oceania; and scholars affiliated with institutions in low-human-development countries. Ninety-eight percent of the published research came from institutions in very-highly-developed countries. Providing evidence that challenges the view that 'talent is the primary driver of scientific advancement' is important because search engines, such as the Web of Science, can modify their algorithms. This would ensure the work of scholars that do not fit the currently dominant profile can have their importance elevated so that their findings can more equitably contribute to knowledge development. This, in turn, will increase the validity of scientific enquiry. Data was collected from Clarivate Analytics

Transparency in data visualization is an essential ingredient for scientific communication. The traditional approach of visualizing continuous quantitative data solely in the form of summary statistics (i.e., measures of central tendency and dispersion) has repeatedly been criticized for not revealing the underlying raw data distribution. Remarkably, however, systematic and easy-to-use solutions for raw data visualization using the most commonly reported statistical software package for data analysis, IBM SPSS Statistics, are missing. Here, a comprehensive collection of more than 100 SPSS syntax files and an SPSS dataset template is presented and made freely available that allow the creation of transparent graphs for one-sample designs, for one- and two-factorial between-subject designs, for selected one- and two-factorial within-subject designs as well as for selected two-factorial mixed designs and, with some creativity, even beyond (e.g., three-factorial mixed-designs). Depending on graph type (e.g., pure dot plot, box plot, and line plot), raw data can be displayed along with standard measures of central tendency (arithmetic mean and median) and dispersion (95% CI and SD). The free-to-use syntax can also be modified to match with individual needs. A variety of example applications of syntax are illustrated in a tutorial-like fashion along with fictitious datasets accompanying this contribution. The syntax collection is hoped to provide researchers, students, teachers, and others working with SPSS a valuable tool to move towards more transparency in data visualization.

Abstract

The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments.

Survey Objectives The 2005 Jamaica Multiple Indicator Cluster Survey has as its primary objectives: - To provide up-to-date information for assessing the situation of children and women in Jamaica. - To furnish data needed for monitoring progress toward goals established by the Millennium Development Goals, the goals of A World Fit For Children (WFFC), and other internationally agreed upon goals, as a basis for future action; - To contribute to the improvement of data and monitoring systems in Jamaica and to strengthen technical expertise in the design, implementation, and analysis of such systems.

Survey Content MICS questionnaires are designed in a modular fashion that can be easily customized to the needs of a country. They consist of a household questionnaire, a questionnaire for women aged 15-49 and a questionnaire for children under the age of five (to be administered to the mother or caretaker). Other than a set of core modules, countries can select which modules they want to include in each questionnaire.

Survey Implementation The survey was carried out by STATIN with the support and assistance of UNICEF and other partners. Technical assistance and training for the surveys is provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.

Geographic coverage

The survey is nationally representative and covers the whole of Jamaica.

Analysis unit

Households (defined as a group of persons who usually live and eat together)

De jure household members (defined as members of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)

Women aged 15-49

Children aged 0-4

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the Jamaica Multiple Indicator Cluster Survey (MICS) was designed to provide estimates on a large number of indicators on the situation of children and women at the national level, as well as urban and rural areas. Parishes were identified as the main sampling domains and were divided into sampling regions of equal sizes. The sample was selected in two stages. Within each sampling region, two census enumeration areas/Primary Sampling Units (PSUs) were selected with probability proportional to size. Using the household listing from the selected PSUs a systematic sample of 6,276 dwellings was drawn.

The sampling procedures are more fully described in the the sampling appendix (appendix A) of the final report.

Sampling deviation

Five of the selected enumeration areas were not visited because they were inaccessible due to flooding during the fieldwork period. Sample weights were used in the calculation of national level results.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Jamaica MICS were structured questionnaires based on the MICS3 Model Questionnaire with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including sex, age, relationship, and orphanhood status. The household questionnaire includes support to orphaned and vulnerable children, education, child labour, water and sanitation, and salt iodization, with optional modules for child discipline, child disability and security of tenure and durability of housing. In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. For children, the questionnaire was administered to the mother or caretaker of the child. The women's questionnaire include women's characteristics, child mortality, tetanus toxoid, maternal and newborn health, marriage, contraception, and HIV/AIDS knowledge, with optional modules for unmet need, domestic violence, and sexual behavior. The children's questionnaire includes children's characteristics, birth registration and early learning, vitamin A, breastfeeding, care of illness, malaria, immunization, and an optional module for child development. All questionnaires and modules are provided as external resources.

Cleaning operations

Data editing took place at a number of stages throughout the processing (see Other processing), including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of SPSS data files

Detailed documentation of the editing of data can be found in the data processing guidelines

Response rate

In the 6,276 dwellings selected for the sample, 5,604 households were found to be occupied (Table HH.1). Of these, 4,767 were successfully interviewed for a household response rate of 85.1 percent. The reason for this lower response rate is given in the previous section. In the interviewed households, 3,777 women (age 15-49) were identified. Of these, 3,647 were successfully interviewed, yielding a response rate of 96.6 percent. In addition, 1,444 children under age five were listed in the household questionnaire. Of these, questionnaires were completed for 1,427 which correspond to a response rate of 98.8 percent.

Overall response rates of 82.1 and 84.1 percent were calculated for the women's and under-5's interviews respectively. Note that the response rates for the Kingston Metropolitan Area (KMA) were lower than in other urban areas and in the rural area. Two factors contributed to this - more dwellings were vacant, often as a result of urban violence, and in the upper income areas access to dwellings was more difficult. In the rural areas, the rains prevented access to some households as some roads were inundated.

Sampling error estimates

Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling errors are the results of mistakes made in the implementation of data collection and data processing. Numerous efforts were made during implementation of the 2005-2006 MICS to minimize this type of error, however, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors can be evaluated statistically. The sample of respondents to the 2005-2006 MICS is only one of many possible samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differe somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability in the results of the survey between all possible samples, and, although, the degree of variability is not known exactly, it can be estimated from the survey results. The sampling erros are measured in terms of the standard error for a particular statistic (mean or percentage), which is the square root of the variance. Confidence intervals are calculated for each statistic within which the true value for the population can be assumed to fall. Plus or minus two standard errors of the statistic is used for key statistics presented in MICS, equivalent to a 95 percent confidence interval.

If the sample of respondents had been a simple random sample, it would have been possible to use straightforward formulae for calculating sampling errors. However, the 2005-2006 MICS sample is the result of a multi-stage stratified design, and consequently needs to use more complex formulae. The SPSS complex samples module has been used to calculate sampling errors for the 2005-2006 MICS. This module uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. This method is documented in the SPSS file CSDescriptives.pdf found under the Help, Algorithms options in SPSS.

Sampling errors have been calculated for a select set of statistics (all of which are proportions due to the limitations of the Taylor linearization method) for the national sample, urban and rural areas, and for each of the five regions. For each statistic, the estimate, its standard error, the coefficient of variation (or relative error -- the ratio between the standard error and the estimate), the design effect, and the square root design effect (DEFT -- the ratio between the standard error using the given sample design and the standard error that would result if a simple random sample had been used), as well as the 95 percent confidence intervals (+/-2 standard errors).

Details of the sampling errors are presented in the sampling errors appendix to the report and in the sampling errors table presented in te external resources.

Data

These are data and codes to replicate the analysis in our paper "Measuring transnational social fields through binational link-tracing sampling "

The National Automotive Sampling System (NASS) Crashworthiness Data System (CDS) is a nationwide crash data collection program sponsored by the U.S. Department of Transportation. It is operated by the National Center for Statistics and Analysis (NCSA) of the National Highway Traffic Safety Administration (NHTSA). The NASS CDS provides an automated, comprehensive national traffic crash database, and collects detailed information on a sample of all police-reported light ]motor vehicle traffic crashes. Data collection is accomplished at 24 geographic sites, called Primary Sampling Units (PSUs). These data are weighted to represent all police reported motor vehicle crashes occurring in the USA during the year involving passenger cars, light trucks and vans that were towed due to damage.

N, number of environmental samples;Q, number of clones sequenced;S, taxonomic richness; OTU, number of OTUs, 97% genetic similarity;HNP, nonparametric Shannon diversity index; Chao1, Chao’s nonparametric estimate of species diversity.

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Medical Active Air Sampling System Market Size 2024-2028

The medical active air sampling system market size is forecast to increase by USD 129.26 million, at a CAGR of 6.71% between 2023 and 2028.

Active air sampling systems have witnessed significant growth In the medical industry due to the increased requirement for microbiological monitoring to ensure patient safety and maintain a clean environment. The integration of the Internet of Things (IoT) and connectivity In these systems enables real-time monitoring and data analysis, enhancing their efficiency and accuracy. However, the high initial investments associated with active air sampling systems may hinder market growth for some healthcare facilities. Despite this challenge, the benefits of these systems, such as early detection and prevention of airborne infections, make them an essential investment for healthcare providers seeking to maintain optimal indoor air quality and protect patient health.

What will be the Size of the Medical Active Air Sampling System Market During the Forecast Period?

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The market encompasses technologies and solutions designed for the detection and analysis of airborne contaminants in healthcare settings. This market exhibits strong growth due to increasing awareness of indoor air quality and the potential health risks associated with airborne pollutants. Factors driving market expansion include the proliferation of advanced technologies, such as real-time monitoring systems and sensor technologies, which enable early detection and mitigation of contaminants. Additionally, the growing prevalence of healthcare-associated infections and the need for stringent regulatory compliance are key market growth drivers.
The market scope includes various applications, such as the detection of bacteria, viruses, and volatile organic compounds (VOCs), including those derived from fuel sources like gasoline, ethanol, methanol, and natural gas. These fuels undergo processes like fermentation, refining, and distillation, producing by-products that can impact indoor air quality. Market trends include the development of multi-parameter monitoring systems and the integration of artificial intelligence and machine learning algorithms for enhanced data analysis and interpretation.

How is this Medical Active Air Sampling System Industry segmented and which is the largest segment?

The medical active air sampling system industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Pharmaceuticals and biotechnology
  Hospitals and clinics
  Others


Type

  Portable microbial sampling system
  Desktop microbial sampling system


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK


  Asia

    China


  Rest of World (ROW)

By Application Insights

The pharmaceuticals and biotechnology segment is estimated to witness significant growth during the forecast period.

In the pharmaceutical and biotechnology sectors, active air sampling systems are essential for maintaining the sterility and quality of production facilities. These systems are crucial in pharmaceutical plants, where the production of medicines and pharmaceutical products directly impacts public health. Biotechnology laboratories, as hubs of innovation, deal with delicate biological materials, cell cultures, and genetic materials that are susceptible to contamination. Active air sampling systems provide real-time data on airborne particles and microorganisms, enabling researchers to maintain experiment sterility and prevent cross-contamination. These systems are particularly important in high-risk locations where contamination can have severe consequences. Active and passive sampling methods, including impaction samplers and settle plates, are used for microbial monitoring.

Calibration, microbial growth identification, and trend analyses are critical components of a monitoring plan. Compliance with regulations, such as those related to environmental monitoring and risk assessment, requires frequent testing and adherence to purity limits. Air sampling techniques, including CFUs (colony-forming units) and quantitative and qualitative results, are essential for ensuring the effectiveness of active air sampling systems.

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The pharmaceuticals and biotechnology segment was valued at USD 180.65 million in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 34% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the

With the ongoing digital transformation, the internet, mobile, social media and other computer technologies are being increasingly used in the targeting, recruitment, transportation and exploitation of human trafficking victims. The current study is the first of its kind which uses a qualitative method to comprehensively investigate the role of technology in the lived experiences of human trafficking victims in Pakistan. This qualitative study was carried out with a phenomenological approach in two provinces of Pakistan. Data was collected using in-depth interviews with 13 victims who were selected using purposive and snowball sampling methods. The data was then analyzed using Braun and Clarke’s thematic analysis approach. The data analysis results were divided into four main themes and ten sub-themes. The main themes are: Recruitment approaches, transportation process, exploitation process, and mental health consequences. The analysis show that traffickers extensively use technology across all phases of human trafficking. This study aims to support a range of relevant institutional stakeholders, including law enforcement agencies, legislative bodies, policymakers, civil society and SDG (2030) goals, in Pakistan and globally, in their efforts to address and enhance the learning and awareness level regarding related technologies in order to improve investigation in combating technological based human trafficking.

Research ships working at sea map the seafloor. The ships collect bathymetry data. Bathymetry is the measurement of how deep the sea is. Bathymetry is the study of the shape and features of the seabed. The name comes from Greek words meaning "deep" and “measure". Backscatter is the measurement of how hard the seabed is. Bathymetry and backscatter data are collected on board boats working at sea. The boats use special equipment called a multibeam echosounder. A multibeam echosounder is a type of sonar that is used to map the seabed. Sound waves are emitted in a fan shape beneath the boat. The amount of time it takes for the sound waves to bounce off the bottom of the sea and return to a receiver is used to find out the water depth. The strength of the sound wave is used to find out how hard the bottom of the sea is. A strong sound wave indicates a hard surface (rocks, gravel), and a weak signal indicates a soft surface (silt, mud). The word backscatter comes from the fact that different bottom types “scatter” sound waves differently. Using the equipment also allows predictions as to the type of material present on the seabed e.g. rocks, pebbles, sand, mud. To confirm this, sediment samples are taken from the seabed. This process is called ground-truthing or sampling. Grab sampling is the most popular method of ground-truthing. There are three main types of grab used depending on the size of the vessel and the weather conditions; Day Grab, Shipek or Van Veen Grabs. The grabs take a sample of sediment from the surface layer of the seabed. The samples are then sent to a lab for analysis. Particle size analysis (PSA) has been carried out on samples collected since 2004. The results are used to cross-reference the seabed sediment classifications that are made from the bathymetry and backscatter datasets and are used to create seabed sediment maps (mud, sand, gravel, rock). Sediments have been classified based on percentage sand, mud and gravel (after Folk 1954). This dataset show locations that have completed samples from the seabed around Ireland. The bottom of the sea is known as the seabed or seafloor. These samples are known as grab samples. This is a dataset collected from 2001 to 2019. It is a vector dataset. Vector data portrays the world using points, lines and polygons (areas). The sample data is shown as points. Each point holds information on the surveyID, year, vessel name, sample id, instrument used, date, time, latitude, longitude, depth, report, recovery, percentage of mud, sand and gravel, description and folk classification. The dataset was mapped as part of the Irish National Seabed Survey (INSS) and INFOMAR (Integrated Mapping for the Sustainable Development of Ireland’s Marine Resource). Samples from related projects are also included: ADFish, DCU, FEAS, GATEWAYS, IMAGIN, IMES, INIS_HYRDO, JIBS, MESH, SCALLOP, SEAI and UCC.

The global powder samplers market is experiencing robust growth, driven by increasing demand across various industries including pharmaceuticals, food processing, and chemicals. The market size in 2025 is estimated at $500 million, exhibiting a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several key factors. Stringent quality control regulations in many sectors necessitate precise and representative sampling, boosting the adoption of advanced powder samplers. Furthermore, automation and digitalization trends are leading to the development of sophisticated samplers offering enhanced accuracy, efficiency, and data integration capabilities. The increasing focus on minimizing human error and improving process optimization across production lines is another major driver. The market is segmented by type (automatic, manual), application (pharmaceuticals, food & beverage, chemicals, etc.), and region. Leading players such as Analytik Jena, Bürkle, and Sentry Equipment are actively involved in product innovation and strategic partnerships to capture market share. Looking ahead to 2033, the market is projected to reach approximately $900 million. However, certain restraints, such as high initial investment costs associated with automated systems and the potential for sampling errors due to variations in powder properties, may temper growth to some extent. Nevertheless, continued technological advancements, such as the integration of advanced sensors and improved data analytics, are expected to mitigate these challenges and support sustained expansion throughout the forecast period. The growing adoption of online sampling techniques and a rising emphasis on safety and hygiene standards across industries will further propel market growth. Regional variations in market penetration will depend on factors like regulatory frameworks, industrial development, and the pace of technological adoption in different parts of the world. This report provides a detailed analysis of the global powder samplers market, projected to be worth over $2 billion by 2028. It examines market dynamics, key players, and future growth opportunities, offering invaluable insights for industry stakeholders. The report delves into various aspects, from concentration and characteristics to emerging trends and growth catalysts, empowering businesses to make informed strategic decisions.

The Reservoir Sampling Services market is experiencing significant growth as the demand for efficient data collection and analysis continues to rise across various industries. This innovative sampling methodology allows businesses to obtain a representative sample from a large dataset without needing to retrieve the

Abstract

This survey was conducted in Slovak Republic between January 2013 and March 2014 as part of the fifth round of the Business Environment and Enterprise Performance Survey (BEEPS V), a joint initiative of the World Bank Group and the European Bank for Reconstruction and Development. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

Data from 276 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.

The survey topics include firm characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement collaboration, security, government policies, laws and regulations, financing, overall business environment, bribery, capacity utilization, performance and investment activities, and workforce composition.

In 2011, the innovation module was added to the standard set of Enterprise Surveys questionnaires to examine in detail how introduction of new products and practices influence firms' performance and management.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not common practice, apart from the construction and agriculture sectors which are not included in the survey.

Regional stratification was defined in four regions (city and the surrounding business area) throughout Slovak Republic.

The database "Albertina Company Monitor" was used as the frame for the selection of a sample with the aim of obtaining interviews at 270 establishments with five or more employees.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 1.9 % (31 out of 1,613 establishments).

In the dataset, the variables a2 (sampling region), a6a (sampling establishment's size), and a4a (sampling sector) contain the establishment's classification into the strata chosen for each country using information from the sample frame. Variable a4a is coded using ISIC Rev 3.1 codes for the chosen industries for stratification. These codes include most manufacturing industries (15 to 37), retail (52), and (45, 50, 51, 55, 60-64, 72) for other services.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different versions of the questionnaire were used. The basic questionnaire, the Core Module, includes all common questions asked to all establishments from all sectors. The second expanded variation, the Manufacturing Questionnaire, is built upon the Core Module and adds some specific questions relevant to manufacturing sectors. The third expanded variation, the Retail Questionnaire, is also built upon the Core Module and adds to the core specific questions.

The innovation module was added to the standard set of Enterprise Surveys questionnaires to examine how introduction of new products and practices influence firms' performance and management.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether, while the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as a different option from don’t know. b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of contacted establishments per realized interview was 0.14. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.56.

The dataset contains details of field collection of groundwater samples with use of different water intake devices and the measurement results of gaseous compounds (methane) obtained during analytical method validation performed in order to develop a methodology of groundwater sampling for analysis of dissolved gases. The dataset is not intended to be used for any site characterisation. Sampling sites were chosen based on high probability of occurrence of measureable methane content in groundwater. Furthermore, the data will be used for formal procedure to obtain the methodology accreditation from the Polish Centre for Accreditation (PCA). The dataset was created within SECURe project (Subsurface Evaluation of CCS and Unconventional Risks) - https://www.securegeoenergy.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 764531